Search Results (5,118)

In industrial quality control, the precise identification of severe structural weld defects is paramount. Traditional 2D image-based detection methods are susceptible to illumination and texture interference, while high-precision 3D laser scanning solutions are costly and impractical for large-scale deployment. To achieve reliable geometric defect detection at low cost, this paper proposes a detection framework based on monocular depth estimation and a dual-attention point cloud network. First, YOLOv8 is employed for rapid region of interest extraction, and an advanced monocular depth estimation model generates 3D pseudo-point clouds containing geometric information. Secondly, addressing the challenge of distinct spatial orientation features in missed weld defects that are prone to confusion, this paper introduces a dual-attention-enhanced point cloud classification network named DA-PointNet++. This model embeds dual-attention modules within the PointNet++ backbone network, enhancing key feature representation in both the channel and spatial dimensions. Experimental results demonstrate that this approach achieves an accuracy of 93.67% and a recall rate of 90.51% in a unified binary classification task for general weld defect detection, effectively identifying both normal welds and complex missed weld defects. Compared to PointConv, Dynamic Graph Convolutional Neural Network (DGCNN), and mainstream Point Cloud Transformer, this method significantly reduces false negative rates while maintaining low computational costs, offering a cost-effective solution for industrial automation. Full article

The growing diversity of wireless standards and complex real-world channel effects render automatic modulation classification (AMC) increasingly challenging for spectrum monitoring and edge intelligence. However, most competitive deep-learning-based AMC networks still require ${10}^5$–${10}^6$ parameters, exceeding the memory available on resource-constrained edge platforms. We propose LGP-Net, a lightweight gated-fusion network that pairs a physics-informed expert branch with a compact temporal encoder built from depthwise separable convolution (DSConv), squeeze-and-excitation (SE) attention, and a single-layer gated recurrent unit (GRU). Specifically, unlike other dual-branch structures that directly concatenate the outputs of both pathways, this work designs a lightweight gating unit that requires no external signal-to-noise ratio (SNR) labels and adaptively reweights the two pathways according to signal-quality degradation. With fewer than 40 K parameters, a peak activation footprint of 26.00 KB and an amortised inference latency of 9.7 $\mathsf{\mu }$s per sample under GPU acceleration, LGP-Net attains 65.00% overall accuracy on RadioML 2016.10B (91.48% at 0 dB) and 62.76% on RadioML 2016.10A, placing it in a competitive accuracy–efficiency regime relative to architectures consuming 5× to 500× more parameters. These characteristics support deployment-oriented feasibility under memory-constrained edge settings and high-throughput spectrum-monitoring pipelines. Full article

Cloud detection in high-resolution Gaofen-1 (GF-1) imagery is challenging due to the absence of short-wave infrared (SWIR) bands, which prevents the use of physically interpretable indices such as the Normalized Difference Snow Index (NDSI) and often leads to severe cloud–snow confusion. To address this limitation, we propose a unified framework, termed the Cross-Modality Spectral Expansion and Dual-Prior Network (CMSE-DPNet), that integrates cross-modality spectral expansion with physical–semantic dual priors. First, an improved CycleGAN reconstructs 13-band pseudo-Sentinel-2 spectra from four-band GF-1 imagery, enabling the computation of snow-sensitive physical indices. Second, a Snow-Aware Feature Attention Guidance Module (SAFAGM) introduces pixel-level physical priors derived from NDSI, while a Label-Guided Channel Attention Module (LG-CAM) injects scene-level semantic priors inferred from geographic metadata using a large language model. These complementary priors guide the network to better distinguish clouds from spectrally similar backgrounds. Experiments on the GF-1 dataset show that the proposed method achieves an F1-score of 94.41% and an Intersection over Union (IoU) of 89.40%, outperforming several state-of-the-art cloud detection methods. The results indicate that cross-modality spectral expansion combined with physical–semantic prior guidance effectively improves cloud detection performance in complex cloud–snow coexistence scenarios. Full article

Using a global panel of bilateral digitally delivered services exports for 192 economies from 2006 to 2022, together with large-scale international news data, this study examines the impact of international media sentiment on digital service exports, with particular attention to the institutional-barrier channel. To address the temporal aggregation mismatch between high-frequency media sentiment and annual trade flows, as well as potential endogeneity concerns, we employ a Mixed Two-Stage Least Squares (M2SLS) approach. The results show that more favorable international media sentiment has a positive and statistically significant effect on digital service exports. This finding remains robust across a range of measurement checks, placebo tests, alternative instrument constructions, subsample analyses, and Bayesian estimation. Further analysis supports an institutional-barrier interpretation by showing that favorable media sentiment is associated with lower bilateral digital service trade policy heterogeneity. The impact is stronger in trust- and reputation-intensive service sectors and in cultural contexts where reputational signals are more salient, while it weakens or reverses in technical service sectors and in highly secular-rational and institutionally asymmetric trading relationships. Full article

Digital camera sensors are designed to capture a wide range of incident illuminants, enabling the creation of high-quality images. However, these sensors lack the capability to differentiate between the color of the source illuminant and the actual color (or original color) of the object being captured. For this reason, the computational color constancy (CCC) was introduced and has been developed over decades. The CCC is an approach to modeling the color perception of the human visual system (HVS) by ensuring accurate object color determination under varying source illuminant conditions. At the core of human visual perception (HVP)-based CCC is attaining higher accuracy in scene illuminant estimation. The emergence of deep convolutional neural networks (DCNNs) was a recent innovation in accurate illuminant estimation, fundamentally transforming the CCC research landscape. Nevertheless, accurate illuminant estimation still remains a huge challenge for both traditional and state-of-the-art (SOTA) approaches. To further advance precision in illuminant estimation, this article presents a novel learning-based illumination color cast estimation approach to HVP-based CCC. Most importantly, the proposed approach is intended to integrate informative features into both channel and spatial regions while preserving long-term dependency feature information with the use of dense skip connections. To achieve these objectives, the proposed Dense Dual Connection Aggregated Transform Network (DDCATNet) architecture is designed to comprise several modules: shallow feature extraction, channel-wise and spatial feature-based Dense Dual Connection (DDC), fusion of the dense channel-wise attention (CA) and spatial attention (SA) branches through a gate mechanism (GM) unit, and aggregate transform. It is worth noting that both the CA blocks and the SA blocks in the DDC module are characterized by dense and cascading connections, meant to preserve long-term feature information and modulate different-level feature information at both global and local scales. The densely connected CA branch (DCA) and the densely connected SA branch (DSA) are also highly effective in securing high-contribution information while suppressing redundant data. The GM unit is integrated at the back of the DDC module, fusing the two DCA and DSA branches to ensure the adaptive merging of useful hierarchical feature information and the extraction of more valuable feature information. As a result, the proposed DDCATNet architecture significantly enhanced precision in illuminant estimation, thereby improving performance. In rigorous experiments on a wide range of datasets, the proposed DDCATNet approach outperformed its SOTA counterparts, validating the efficacy and generalization capabilities, as well as robust camera-invariance, across diverse, single- and multi-illuminant datasets and model architectures. Full article

Drug-induced QT interval prolongation is a critical safety concern in drug development, yet accurate and mechanistically interpretable prediction from chemical structure remains challenging due to the limited substructural resolution of existing approaches. Here, we present a motif-level graph learning framework for interpretable QT risk prediction. In this framework, molecules are decomposed into chemically meaningful motifs, enabling representation at an intermediate structural scale between atoms and predefined structural alerts. Motif features are encoded using a pre-trained chemical language model, and inter-motif relationships are modeled via attention-based graph learning with cross-scale integration. The model is trained and evaluated on two clinically grounded datasets derived from regulatory drug labeling (DIQTA) and real-world pharmacovigilance data (FAERS), achieving strong and consistent predictive performance with robust generalization across data sources. Importantly, motif-level attention reveals that QT liability is associated with the cooperative organization of compact cationic centers and heteroatom-rich, conformationally adaptable scaffolds, rather than isolated functional groups. These patterns are consistent with known determinants of human ether-à-go-go-related (hERG) channel blockade while providing a more structured and chemically specific interpretation beyond conventional structural alerts. Overall, this work establishes a generalizable and interpretable framework for QT risk prediction and highlights motif-level graph learning as an effective strategy for structure-based modeling of adverse drug reactions. Full article

In automotive crash testing, trustworthy crash-test curves are essential for reliable crashworthiness assessment, yet automated anomaly detection is difficult due to limited labeled abnormal cases, event-level data scarcity, and distribution shifts across vehicle models and sensor configurations. This paper proposes MVCA-AD (Multi-View Context Augmentation for Anomaly Detection) for single-channel crash-test curves. MVCA-AD generates multiple context-rich views using deterministic time- and frequency-domain transformations to amplify subtle anomalous patterns under limited labeled supervision. A trend-aware modulation module and cross-view attention fuse these views to improve sensitivity to critical segments such as impact spikes and gradual transitions while remaining robust to noise. Experiments on three subsets derived from physical full-scale crash tests show that MVCA-AD improves Precision, Recall, F1-score, and area under the ROC curve (AUC) over strong baselines and achieves stable performance under event-level grouped evaluation across heterogeneous head and B-pillar crash-test signals. The proposed approach supports crash-test data quality control by automatically identifying abnormal curves for downstream crashworthiness assessment workflows. Full article

Nowadays, remote sensing images are characterized by significant scale variations, a high density of small targets, and complex background conditions, which pose substantial challenges for small-object detection. To address these issues, we propose EMWMS-YOLO, a lightweight and efficient detection framework built upon YOLOv11n. Specifically, an Efficient Multi-Scale Cross-Layer Extraction (EMSCLE) backbone is designed by integrating the Dual-Branch Feature Extraction (DBFE), Multi-Scale Feature Perception (MSFP), and Spatial Pyramid Pooling Fast with Large Separable Kernel Attention (SPPF-LSKA) modules, enabling effective multi-scale feature extraction and cross-channel interaction. Furthermore, a Multi-Scale Adaptive Feature Fusion (MSAFF) neck architecture, composed of the Channel-Enhanced Convolution (CEC) and Multi-Scale Gated Feature Fusion (MSGFF) modules, is introduced to dynamically fuse cross-scale features and enhance salient target responses while suppressing background noise. In addition, the WaveletPool module replaces conventional pooling operations to reduce information loss and feature aliasing while preserving structural details. A Detect-MultiSEAM detection head is constructed by embedding a multi-scale spatial enhancement attention mechanism, which improves feature representation under complex conditions and reduces missed detections and false positives. Finally, the ShapeIoU loss function is employed to better model geometric and morphological properties, thereby improving localization accuracy. Experimental results on the VEDAI and NWPU-VHR-10 datasets demonstrate that the proposed method achieves improvements of 9.8% and 4.1% in mAP50 over the YOLOv11n baseline, respectively, verifying its effectiveness in small-object detection. Full article

Steel structures in complex service environments are highly susceptible to corrosion, making accurate detection challenging. This study proposes an improved YOLOv26-based method for corrosion damage segmentation. A diverse dataset is constructed by combining field-collected and public data with varying lighting conditions and multi-scale features. Enhancements to the YOLOv26-seg architecture include integrating Efficient Channel Attention (ECA) in the backbone to strengthen low-contrast feature representation, designing a multi-branch attention mechanism (ECA + CBAM) in the detection head to improve small- and medium-scale target recognition, and introducing Selective Kernel Attention (SKA) in the segmentation branch to refine boundary details. The resulting YOLOv26-ECS model achieves an mAP50 of 0.920 and mAP50–95 of 0.851 on the self-constructed dataset, outperforming the baseline by 5.0% and 6.0%, respectively, while maintaining 28.34 FPS. Experiments on public datasets further demonstrate strong generalization. A GUI system is also developed for visualization and practical deployment. Overall, the proposed method delivers accurate and efficient corrosion detection and segmentation, showing strong potential for engineering applications. Full article

Background/Objectives: Epiretinal membrane (ERM) is a common vitreoretinal disorder characterized by fibrocellular proliferation on the inner retinal surface, often leading to progressive visual impairment. Accurate grading of ERM severity using optical coherence tomography (OCT) is critical for treatment planning and surgical decision-making; however, manual grading is labor-intensive and subjective. This study aims to develop an automated and reliable deep learning-based method for ERM severity classification. Methods: We propose DenseViT-OCT, a hybrid deep learning model that integrates dense convolutional neural networks (CNN) and vision transformers (ViT). The model introduces three key modules: Multi-Scale Dense Feature Aggregation (MDFA) for capturing hierarchical features across multiple spatial scales, Adaptive Feature Calibration (AFC) for enhancing feature discrimination through channel and spatial attention, and Cross-Attention Feature Fusion (CAFF) for enabling bidirectional interaction between convolutional and transformer representations. The model was trained and evaluated on 2195 OCT B-scan images obtained from 397 patients. Results: DenseViT-OCT achieved an overall accuracy of 94.76% on the internal four-class test set, outperforming 19 benchmark models, including ConvNeXt, EfficientNet, ViT, and Swin Transformers. The model demonstrated balanced performance with a macro-averaged precision of 93.76%, recall of 93.22%, F1-score of 93.47%, Cohen’s kappa of 92.62%, and macro-Area Under the Curve (AUC) of 98.95%. Ablation experiments confirmed the contribution of the proposed MDFA, AFC, CAFF, and deep supervision components, with the full model consistently outperforming reduced variants and standalone DenseNet121 and ViT-B/16 backbones. In repeated experiments across five random seeds, DenseViT-OCT also achieved the best mean accuracy (0.9399 ± 0.0052). External validation on the public multicenter OCTDL dataset, performed as binary ERM-versus-normal classification because of label availability, yielded 90.76% accuracy and 97.61% AUC, indicating promising generalization beyond the development cohort. Conclusions: DenseViT-OCT provides a robust framework for automated ERM severity classification from OCT B-scans. The combination of local CNN features, global transformer context, and dedicated fusion modules improves classification performance and yields clinically meaningful error patterns. Although further stage-wise multicenter validation, volumetric OCT analysis, and prospective clinical assessment are required, the proposed method shows promise as a research-oriented decision-support framework for B-scan-level ERM assessment. Full article

Accurate tomato maturity detection is critical for optimizing key agricultural operations in precision agriculture, including harvesting, grading, and quality control. Despite advances in deep learning and machine vision, reliable detection in real-world environments remains challenging due to cluttered backgrounds, dense fruit clustering, and subtle color differences between maturity stages. In response to these challenges, we present TAA-YOLOv8, an attention-enhanced detection architecture integrating a novel Tomato-Adaptive Attention (TAA) module that performs sequential channel–spatial feature refinement using an adaptive 1D convolution for channel recalibration and a balanced 5 × 5 spatial kernel for improved localization, enhancing discriminative representation while preserving computational efficiency. The framework is evaluated on three datasets representing diverse agricultural environments: a newly introduced Cross-Regional Tomato dataset collected from open-field farms in Bangladesh and greenhouse facilities in Japan, and two public benchmarks, Laboro Tomato and Tomato Plantfactory. TAA-YOLOv8m outperforms baseline YOLOv8m, achieving mAP@50–95 improvements of +9.29%, +9.00%, and +6.65% with F1-scores of 0.968, 0.976, and 0.955, respectively. It further surpasses attention-enhanced variants and RT-DETR-L, and remains competitive with YOLOv11m. Gradient-Weighted Class Activation Mapping (Grad-CAM) shows concentrated fruit-centered activations, providing transparent decision-making evidence and supporting stakeholder confidence in practical deployment within vision-based agricultural management systems. Full article

With the widespread adoption of multiple-input–multiple-output (MIMO) technology, channel state information (CSI) prediction has become a crucial technique for enhancing the performance of wireless communication systems. Traditional channel prediction methods face performance bottlenecks under high-speed mobility and complex channel conditions, making it difficult to meet the requirements of modern communication systems. To address this issue, this paper proposes a fully complex-valued cross-domain modeling framework, termed a complex-valued multi-scale transformer with time–frequency cross-attention network (CMTF-Net), for MIMO CSI prediction. CMTF-Net integrates a learnable multi-scale short-time Fourier transform (LMS-STFT), complex-valued multi-head self-attention (C-MHSA), and bidirectional cross-domain attention for complex-valued sequences (BCDA-CVS). These modules are designed to preserve amplitude–phase consistency, adapt time–frequency representations to CSI evolution, and enable information interaction between temporal and spectral features. On the simulated Overall Test set, CMTF-Net achieves the lowest MAE of 0.000032 and the highest Corr. ($\rho$) of 0.8230 among the compared methods, while maintaining competitive SE and BER values of 0.4240 and 0.2411 at SNR = 10 dB. On the DICHASUS measured datasets, CMTF-Net also shows favorable Test-ID and Test-OOD performance. For example, on DICHASUS-2186, it obtains Corr. ($\rho$)/SE/BER values of 0.8367/0.4935/0.2243 on Test-ID and 0.8061/0.4697/0.2351 on Test-OOD. These results indicate that CMTF-Net provides a balanced performance profile across prediction accuracy, spatial alignment, and communication-oriented evaluation. Full article

Spiking neural networks (SNNs) have emerged as a promising paradigm for energy-efficient neuromorphic computing, particularly when processing asynchronous event streams from dynamic vision sensors (DVSs). However, SNNs often suffer from limited representational capacity and suboptimal feature recalibration compared to their artificial counterparts. To address these challenges, we propose SE-SNN, a novel architecture that integrates Squeeze-and-Excitation (SE) blocks into deep residual SNNs, enabling channel-wise attention without spike generation. Furthermore, we introduce a Robust Parametric Leaky Integrate-and-Fire (RobustPLIF) neuron model with learnable membrane time constant ($\tau$) and firing threshold ($v_{\mathrm{th}}$), allowing adaptive temporal dynamics in each layer. Our model is trained on the CIFAR10-DVS dataset.The experimental results demonstrate that SE-SNN achieves an accuracy of $78.8$% on CIFAR10-DVS with 16 time steps, outperforming baseline SNNs while maintaining biological plausibility and hardware efficiency. Ablation studies confirm the individual contributions of the SE blocks and learnable neuron parameters to the performance gains. Full article

Emerging contaminants (ECs) encompass a wide spectrum of pollutants, from endocrine disruptors and persistent organic pollutants to microplastics and pharmaceutical residues. These contaminants often exhibit distinct chemical and physical properties compared with traditional pollutants and potentially pose risks to human health, especially as they have become pervasive in environmental and biological systems. ECs can also pose a significant threat to cardiovascular health, as they may target the ion channels that are critical to regulating cardiac excitability and contraction. However, the impact of ECs on the cardiovascular system, particularly on cardiac ion channels, remains elusive. In this review, we aim to provide an overview of the knowledge base concerning the impact of emerging contaminants on cardiac ion channels, with an emphasis on the effects of these compounds on cardiac excitability, contractility, and overall cardiovascular function. We first outline the structural and functional characteristics of ion channels, along with how these transmembrane proteins regulate cardiac physiology. Subsequently, we detail how typical ECs directly or indirectly interact with various ion channels—including sodium, calcium, potassium channels, as well as ion transporters and exchangers. Special attention is given to studies that have demonstrated cell-level responses or examined how pollutant concentration and chemical structure affect the modulation of ion channels. This review compiles recent research reports to elucidate the mechanisms by which EC exposure disrupts cardiac ion channels, potentially leading to cardiotoxicity. Moreover, the insights gathered herein illuminate critical research gaps and outline essential directions for future investigations. Full article

Robust vessel detection from low-Signal-to-Noise Ratio (SNR) Distributed Acoustic Sensing (DAS) data benefits from exploiting spatial correlations among adjacent channels. The Cross-Channel Attention Fusion Network (CASFNet) is presented, utilizing a Cross-Channel Attention Fusion (CASF) mechanism to dynamically model dependencies among adjacent channels. This approach, based on a dual-component spectrogram representation, adaptively fuses local spatial context, enhancing signal coherence under low-SNR conditions. Experiments on real-world DAS data demonstrate superior accuracy and robustness compared to state-of-the-art methods, achieving a detection accuracy of 99.24% and an F1-score of 99.19%. Ablation results confirm the effectiveness of this spatial fusion strategy for vessel monitoring using submarine DAS data. Full article